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Full-Stack application that allows client to use a predictive model to determine which user is more likely to have tweeted a given text. This project covers everything from API's to Predictive Modeling, SQLAlchemy database storage, Flask, along with other full-stack components. In the end it is deployed for online usage using Heroku.

License: Other

Python 77.41% HTML 22.31% Procfile 0.28%
flask-sqlalchemy heroku-deployment logistic-regression predictive-modeling scikit-learn-python spacy-nlp sql tweepy-api

tweetypy's Introduction

TweetyPy

  • This application allows a client to use a predictive model to determine which user is more likely to have tweeted a given text
  • Developed framework using Flask Python that queries the Twitter API for tweets from various users
  • Implemented word2vect using a SpaCy NLP model to create embeddings from the tweet text
  • Stored embedded tweet data in a SQLAlchemy Database
  • Fit Scikit-Learn Logistic Regression model to tweet data to make predictions, serializing the results for online use

app.py Project Main File

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models.py Database

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twitter.py Natural Language Processing

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Deployed Preview

Screenshot 2022-08-05 062950

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